The influence of age and sex on the prognostic importance of traditional cardiovascular risk factors, selected circulating biomarkers and other markers of subclinical cardiovascular damage

Triglyceride-glucose index improves risk prediction beyond traditional risk factors and hypertension mediated organ damage in healthy adults

BACKGROUND AND AIMS

Triglyceride-glucose (TyG) index, a surrogate measure of insulin resistance, is associated with hypertension mediated organ damage (HMOD) and cardiovascular disease. This study investigated the association between TyG index and major adverse cardiovascular events (MACE) and its interaction with traditional risk factors and HMOD.

METHODS AND RESULTS

Healthy subjects recruited from the general population were thoroughly examined and followed for MACE using nation-wide registries. Cox proportional hazard models were used to calculate the association between TyG index and MACE occurrence. Models were adjusted for Systematic Coronary Risk Evaluation (SCORE) risk factors, pulse wave velocity, left ventricular mass index, carotid atherosclerotic plaque status, and microalbuminuria. Continuous net reclassification and Harrell's Concordance index (C-index) were used to assess the added prognostic value of TyG index. During a follow-up period of mean 15.4 ± 4.7 years, MACE were observed in 332 (17%) of 1970 included participants. TyG index was associated with MACE; HR = 1.44 [95%CI:1.30-1.59] per standard deviation. After adjustment for traditional cardiovascular (CV) risk factors, HR was 1.16 [95%CI:1.03-1.31]. The association between TyG index and MACE remained significant after further adjustment for each HMOD component. However, this finding was evident only in subjects aged 41 or 51 years (HR = 1.39; 95%CI:1.15-1.69). Including TyG index in a risk model based on traditional CV risk factors improved C-index with 0.005 (P = 0.042).

CONCLUSION

In this population-based study of healthy middle-aged subjects, TyG index was associated with MACE independently of traditional CV risk factors and HMOD. TyG index may have a potential role in future risk prediction systems.

N-Terminal Pro-Brain Type Natriuretic Peptide Predicts Cardiovascular Events Independently of Arterial Stiffness, Assessed By Carotid-to-Femoral Pulse Wave Velocity, in Apparently Healthy Subjects

AIM

This study aimed to evaluate whether N-terminal pro-brain natriuretic peptide (NT-proBNP) and carotid-to-femoral pulse wave velocity (PWV) carried independent prognostic value in predicting cardiovascular events in apparently healthy individuals beyond traditional risk factors.

METHODS

A total of 1,872 participants aged 41, 51, 61, or 71 years from the MONItoring of trends and determinants in CArdiovascular disease (MONICA) study were included. Traditional risk factors were assessed, including: smoking status; mean systolic and diastolic blood pressure; body mass index; fasting plasma glucose; serum triglycerides; total, high-density, and low-density lipoprotein cholesterol; NT-proBNP; and PWV. The principal endpoint that was assessed during 16 years of follow-up was a composite of major adverse cardiovascular events (MACE). The secondary endpoints were cardiovascular mortality (CVM), hospitalisation for coronary artery disease (CAD), and a composite of hospitalisation for heart failure (HF) or atrial fibrillation (AF).

RESULTS

At baseline, NT-proBNP was associated with PWV (β=0.14; p<0.001), but not after adjustment for traditional risk factors (β=-0.01; p=0.67). In models including traditional risk factors and PWV, NT-proBNP was associated with all four outcomes (HRMACE=1.33, 95% CI 1.16-1.52; HRCVM=2.02, 95% CI 1.65-2.48; HRCAD=1.29, 95% CI 1.07-1.55; and HRHF or AF=1.79, 95% CI 1.40-2.28). In the same model, PWV was only associated with CVM (HRCVM=1.20, 95% CI 1.01-1.41). No interactions between NT-proBNP and PWV were found. N-terminal pro-brain natriuretic peptide significantly improved net reclassification (NRI) for MACE (NRI=0.12; p=0.03), CVM (NRI=0.33; p<0.001), and HF or AF (NRI=0.33; p<0.001) beyond traditional risk factors, while PWV did not aid in net reclassification improvement for any endpoint.

CONCLUSIONS

In apparently healthy individuals, NT-proBNP and PWV predicted cardiovascular events independently. N-terminal pro-brain natriuretic peptide improved reclassification for the prediction of MACE, CVM, and hospitalisation for HF or AF beyond traditional risk factors, while PWV did not.

A robust framework for enhancing cardiovascular disease risk prediction using an optimized category boosting model

Cardiovascular disease (CVD) is a leading cause of mortality worldwide, and it is of utmost importance to accurately assess the risk of cardiovascular disease for prevention and intervention purposes. In recent years, machine learning has shown significant advancements in the field of cardiovascular disease risk prediction. In this context, we propose a novel framework known as CVD-OCSCatBoost, designed for the precise prediction of cardiovascular disease risk and the assessment of various risk factors. The framework utilizes Lasso regression for feature selection and incorporates an optimized category-boosting tree (CatBoost) model. Furthermore, we propose the opposition-based learning cuckoo search (OCS) algorithm. By integrating OCS with the CatBoost model, our objective is to develop OCSCatBoost, an enhanced classifier offering improved accuracy and efficiency in predicting CVD. Extensive comparisons with popular algorithms like the particle swarm optimization (PSO) algorithm, the seagull optimization algorithm (SOA), the cuckoo search algorithm (CS), K-nearest-neighbor classification, decision tree, logistic regression, grid-search support vector machine (SVM), grid-search XGBoost, default CatBoost, and grid-search CatBoost validate the efficacy of the OCSCatBoost algorithm. The experimental results demonstrate that the OCSCatBoost model achieves superior performance compared to other models, with overall accuracy, recall, and AUC values of 73.67%, 72.17%, and 0.8024, respectively. These outcomes highlight the potential of CVD-OCSCatBoost for improving cardiovascular disease risk prediction.